Identification of traffic accident spots play a pivotal role in planning of roads and application of effective strategies in order to minimize the traffic accidents. This study puts into use the spatial distribution of the traffic accidents scattered throughout the area using spatial analysis and statistical approaches. The purpose of this research study is to analyze the traffic accidents occurring in the Hayatabad area of Peshawar. The fundamental objective of this study is to detect accidents hotspot in an observed area by a complex statistical algorithm. A methodology was developed in ArcGIS 10.2 to analyze the spatial patterns of traffic accidents and to identify hotspots. This study has conducted NNHA spatial clustering method in CrimeSTAT for the identification of hotspot clusters for accidents points in ArcGIS. Moreover, based on the detected hotspots, spatio-temporal tool like Kernel Density Estimation (KDE) analysis was performed in Crime STAT to create a temporal map of RTAs hotspots in ArcGIS. A geostatistical method known as Kriging Interpolation method (KI) was also used to assess the results computed by KDE. The results indicated that the roundabouts located in this area are the major hotspot of accidents, which includes Bagh-e-Naran roundabout, Phase-6 roundabout, Tatara Park roundabout and Jamrud road. Comparison of KDE and KI was performed and it was found that KI outperforms KDE in identifying hotspots. It has been concluded that these hotspots lacked the basic traffic controlling devices, which are necessary for controlling the speed and converging or merging of vehicles at these locations.
Muhammad Babar Ali Rabbani Prof. Dr. Sher Afzal Khan Dr. Qaiser Iqbal and Engineer Qamar Zaman Analysis of Vehicle Accidents using Spatio-temporal Tools in ArcGIS; A Case Study of Hayatabad P International Journal of Engineering Works Vol. 6 Issue 12 PP. 439-444 December 2019
[1] Ahmed, Aizaza. "Road safety in Pakistan." National Road Safety Secretariat, Islamabad, 142, 2007.
[2] Chen, Yen-Chi. "A tutorial on kernel density estimation and recent advances." Biostatistics & Epidemiology 1.1, 161-187, 2017.
[3] Chainey, Spencer, and Jerry Ratcliffe. GIS and crime mapping. John Wiley & Sons, 2013
[4] Gudes, Ori, and Richard Varhol. "Using a spatial analysis approach to investigate Articulated Heavy Vehicle." Journal of Transport Geography 31, 64-71. 2015
[5] Goovaerts, Pierre. Geostatistics for natural resources evaluation. Oxford University Press on Demand, 1997.
[6] Hart, Timothy C., and Paul A. Zandbergen. "Effects of data quality on predictive hotspot mapping." Final report submitted to the National Institute of Justice 1, 2012.
[7] Kazmi, Jamil H., and Salman Zubair. "Estimation of vehicle damage cost involved in road traffic accidents in Karachi, Pakistan: a geospatial perspective." Procedia engineering 77, 70-78, 2014
[8] McCullagh, Michael J. "Detecting hotspots in time and space." ISG06 349 : 1-17, 2006
[9] Nakaya, Tomoki, and Keiji Yano. "Visualising crime clusters in a space‐time cube: An exploratory data‐analysis approach using space‐time kernel density estimation and scan statistics." Transactions in GIS 14.3: 223-239, 2010
[10] Prasannakumar, V., et al. "Spatio-temporal clustering of road accidents: GIS based analysis and assessment." Procedia-Social and Behavioral Sciences 21, 317-325, 2011
[11] World Health Organization. Global status report on road safety 2015. World Health Organization, 2015.
[12] World Health Organization. Global status report on road safety 2018. World Health Organization, 2018.
[13] Razzak, Junaid Abdul, et al. "A successful model of road traffic injury surveillance in a developing country: process and lessons learnt." BMC public health 12.1, 357.2012
[14] Rosenblatt, Murray. "Remarks on some nonparametric estimates of a density function." The Annals of Mathematical Statistics 832-837, 1956
[15] Shafabakhsh, Gholam Ali, Afshin Famili, and Mohammad Sadegh Bahadori. "GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran." Journal of traffic and transportation engineering (English edition) 4.3, 290-299, 2017
[16] Thakali, Lalita, Tae J. Kwon, and Liping Fu. "Identification of crash hotspots using kernel density estimation and kriging methods: a comparison." Journal of Modern Transportation23.2, 93-106, 2015
[17] Van Patten, Isaac T., Jennifer McKeldin-Coner, and Deana Cox. "A microspatial analysis of robbery: Prospective hot spotting in a small city." Crime Mapping: A journal of research and practice 1.1, 7-32., 2009
[18] Jia, Ruo, Anish Khadka, and Inhi Kim. "Traffic crash analysis with point-of-interest spatial clustering." Accident Analysis & Prevention 121, 223-230, 2018
[19] Romano, Benjamin, and Zhe Jiang. "Visualizing traffic accident hotspots based on spatial-temporal network kernel density estimation." Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2017.
[20] Rushton, G., and C. Tiwari. "Spatial filtering/kernel density estimation.", 359-364. 2009
